Analyzing digital holographic microscopy data for hematology applications

ABSTRACT

A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a plurality of DHM images acquired using a digital holographic microscopy system. One or more connected components are identified in each of the plurality of DHM images and one or more training white blood cell images are generated from the one or more connected components. A classifier is trained to identify a plurality of white blood cell types using the one or more training white blood cell images. The classifier may be applied to a new white blood cell image to determine a plurality of probability values, each respective probability value corresponding to one of the plurality of white blood cell types. The new white blood cell image and the plurality of probability values may then be presented in a graphical user interface.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/318,831 filed Dec. 14, 2016, which is a national phase filing under35 U.S.C. § 371 of international patent application no.PCT/US2015/035945 filed Jun. 16, 2015, which claims the benefit of U.S.provisional application Ser. No. 62/012,636 filed Jun. 16, 2014, whichis incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to analyzing digitalholographic microscopy (DHM) for hematology applications. The varioussystems, methods, and apparatuses described herein may be applied to,for example, red blood cell (RBC) volume measurement and white bloodcell (WBC) differential (cell type classification) applications.

BACKGROUND

Digital holographic microscopy (DHM), also known as interference phasemicroscopy, is an imaging technology that provides the ability toquantitatively track sub-nanometric optical thickness changes intransparent specimens. Unlike traditional digital microscopy, in whichonly intensity (amplitude) information about a specimen is captured, DHMcaptures both phase and intensity. The phase information, captured as ahologram, can be used to reconstruct extended morphological information(such as depth and surface characteristics) about the specimen using acomputer algorithm. Modern DHM implementations offer several additionalbenefits, such as fast scanning/data acquisition speed, low noise, highresolution and the potential for label-free sample acquisition.

Conventional cellular analysis techniques such as volume measurement andclassification rely on two-dimensional cellular images that lacktopographical information. Thus, while these techniques may analyze acell based on information such as intensity, their accuracy is limiteddue to a lack of knowledge of the size and shape of the cell.Accordingly, it is desired to provide cellular analysis techniques whichare applicable to imaging modalities such as DHM that provide moredetailed information regarding cellular structure.

SUMMARY

Embodiments of the present invention address and overcome one or more ofthe above shortcomings and drawbacks, by providing methods, systems, andapparatuses related to analyzing digital holographic microscopy (DHM)for hematology applications. Additionally, as explained in furtherdetail in the disclosure, the technology described herein may be appliedto other clinical applications as well.

The ability of DHM to achieve high-resolution, wide field imaging withextended depth and morphological information in a potentially label-freemanner positions the technology for use in several clinicalapplications. For example, in the area of hematology DHM may be used forred blood cell (RBC) volume measurement, white blood cell (WBC)differential (cell type classification). For urine sediment analysis DHMallows for scanning a microfluidic sample in layers to reconstruct thesediment (possibly without waiting for sedimentation); improving theclassification accuracy of sediment constituents. DHM may also be usedfor tissue pathology applications through utilization of extendedmorphology/contrast of DHM (e.g. to discriminate cancerous from healthycells, in fresh tissue, without labeling). Similarly, for rare celldetection applications may utilize extended morphology/contrast of DHM(e.g. to differentiate rare cells such as circulating tumor/epithelialcells, stem cells, infected cells, etc.).

According one aspect of the present invention, as described in someembodiments, a method for analyzing digital holographic microscopy (DHM)data for hematology applications includes receiving DHM images acquiredusing a digital holographic microscopy system. One or more connectedcomponents are identified in each of the plurality of DHM images. One ormore training white blood cell images are generated from the one or moreconnected components and a classifier is trained to identify white bloodcell types using the one or more training white blood cell images. Whena new DHM image is received, a new white blood cell image is extractedfrom the DHM image. Then classifier may then be applied to the new whiteblood cell image to determine probability values, with each respectiveprobability value corresponding to one of the white blood cell types.The new white blood cell image and the plurality of probability valuesmay then be presented in a graphical user interface. In someembodiments, a complete blood cell (CBC) test may be performed using theprobability values.

Various enhancements, modifications, or additions may be made to theaforementioned in different embodiments of the present invention. Forexample, in some embodiments, prior to identifying the one or moreconnected components, a thresholding is applied to the each of theplurality of DHM images to highlight bright spots in each respective DHMimage. Components having a size below a predetermined threshold value(i.e., small connected components) may then be removed. In someembodiments, the classifier is a K-Nearest Neighbor (K-NN) classifier.Such a classifier may use texton-based texture features extracted fromeach of the plurality of DHM images to classify the new DHM image. Inother embodiments, the classifier is a visual vocabulary dictionary(e.g., vocabulary histogram) trained using hierarchical k-means and ascale-invariant feature transform (SIFT) descriptor as a local imagefeature. For example, dense SIFT descriptors may be extracted from eachof the plurality of DHM images and used to construct a binary searchtree representative of a vocabulary dictionary structure. The visualvocabulary dictionary can then be generated based on the binary searchtree. A one against one n-label supporting vector machine (SVM) may beused for identifying one or more of the plurality of white blood celltypes in the DHM images. In still other embodiments, the classifier is adeep learning classifier trained using an auto-encoder convolutionalneural network (CNN).

Additionally, in some embodiments of the aforementioned method, adigital staining technique is applied to the white blood cell images.For example, in one embodiment, a mapping is determined between opticaldensity and coloring associated with a staining protocol. Opticaldensity information associated with the new white blood cell image isalso determined. Prior to presenting the new white blood cell image, thenew white blood cell image is colorized using the mapping and theoptical density information.

According to another aspect of the present invention, as described insome embodiments, an article of manufacture for analyzing digitalholographic microscopy (DHM) data for hematology applications comprise anon-transitory, tangible computer-readable medium holdingcomputer-executable instructions for performing the aforementionedmethod. This article of manufacture may further include instructions forany of the additional features discussed above with respect to theaforementioned method.

In other embodiments of the present invention, a system for analyzingdigital holographic microscopy (DHM) data for hematology applicationscomprises a networking component, a modeling processor, and a graphicaluser interface. The networking component is configured to communicatewith a digital holographic microscopy system to retrieve training DHMimages and a test DHM image. The modeling processor is configured to:identify one or more connected components in each of the training DHMimages, generate one or more training white blood cell images from theone or more connected components, and train a classifier to identifywhite blood cell types using the one or more training white blood cellimages. The modeling processor is further configured to extract a testwhite blood cell image from the test DHM image, and apply the classifierto the test white blood cell image to determine probability values, witheach respective probability value corresponding to one of the whiteblood cell types. The graphical user interface is configured to presentthe test white blood cell image and the probability values.

Additional features and advantages of the invention will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following Figures:

FIG. 1 provides an illustration of a framework for processing DHM imagesfor erythrocyte volume computation, according to some embodiments;

FIG. 2 provides an example illustration of segmentation of erythrocytesin optical path difference DHM (OPD-DHM) images, as may performed insome embodiments;

FIG. 3 shows a geometry model of a normal erythrocyte, as may beutilized in some embodiments;

FIG. 4 provides an example of the parametric modeling of theerythrocytes which illustrates the regularization;

FIG. 5 provides an illustration of a process for calculating cellvolume, according to some embodiments;

FIG. 6 provides an illustration of a pre-processing framework forclassifying white blood cells, as it may be applied in some embodiments;

FIG. 7 shows a sample of the white blood cells used in training andtesting, according to some embodiments;

FIG. 8A provides a table showing an example of a data set that may beused for training and testing for each cell type, as may be used in someembodiments;

FIG. 8B shows pairwise classification results obtained using textonfeatures and KNN classifier for this sample case, as may be obtainedaccording to some embodiments;

FIG. 9 provides an example of local image feature sampling, as it may beapplied in some embodiments;

FIG. 10 shows a complete binary search tree structure which represents avocabulary dictionary structure;

FIG. 11 provides a visualization of the workflow for the transformationfrom local image features (dense SIFT descriptor) to global imagefeatures (vocabulary histogram), as may be applied in some embodiments;

FIG. 12 shows Pairwise Classification results using SIFT and SVMclassification, obtained according to some embodiments;

FIG. 13 shows the structure of a feed-forward neural network with onehidden layer that may be utilized in some embodiments;

FIG. 14 shows a GUI which provides for results visualization andfacilitates user interaction and correction, according to someembodiments;

FIG. 15 illustrates an exemplary computing environment within whichembodiments of the invention may be implemented

DETAILED DESCRIPTION

The following disclosure describes the present invention according toseveral embodiments directed at methods, systems, and apparatusesrelated to analyzing digital holographic microscopy (DHM) for hematologyand other clinical applications. Briefly, the techniques discussedherein comprise of three principle ideas that may be applied indifferent configurations in the various embodiments described herein.First, in some embodiments, red blood cell (RBC) volume estimation isperformed using parametric modeling of erythrocytes to regularize theraw DHM data to match physical models of the erythrocytes, in an attemptto eliminate any distortion caused during the image acquisition process.Second, in some embodiments, label-free differentiation of white bloodcells (WBCs) captured by DHM is performed using a machine learningalgorithm which extract characteristic topological changes from a set oftraining samples DHM images for each sub-type of WBCs (namely,monocytes, neutrophils, basophils, lymphocytes, eosinophils). Themachine learning algorithm then classifies a new cell into one of thecategories automatically based on the learnt DHM image based features.Third, in some embodiments, “digital staining” or “pseudo-coloring” ofthe classified DHM images of the cells is performed to resembleconventional staining techniques. The digital staining method describedherein uses a matched pair of DHM and stained images of a set of cells,and a regression function is learnt to map the DHM image pixel(representing the topology of the cell constituents to a RGB colorscheme of conventional staining techniques).

FIG. 1 provides an illustration of a framework 100 for processing DHMimages for erythrocyte volume computation, according to someembodiments. Briefly, a DHM System 105 is used to acquire one or moreinput images. The Digital Holographic Microscopy System 105 may be anysystem known in the art capable of acquiring DHM images. An ImageSegmentation Component 110 performs segmentation of the erythrocytes inthe acquired input images. Next, a Parametric Modeling Component 115develops a model of the erythrocytes (e.g., using Cassini ovals). Then,a Parametric Modeling Component 120 calculates thickness information ofeach erythrocyte which, in turn, can be used to determine theerythrocyte's volume.

Various segmentation techniques may be applied to the input image tosegment the erythrocytes. For example, in some embodiments, thesegmentation problem performed by the Image Segmentation Component 110is formulated as an energy minimization problem that minimizes thepiecewise constant Mumford-Shah energy functional in a combinatorialoptimization framework. The segmentation energy can be described asfollows. Given an image u and an image of interest u: Ω→R, where Ω is anopen bounded subset in R2 that comprises several connected components Ω,and bounded by a closed boundary C=∂Ω, find a piecewise constantapproximation such that u is constant within the components Ω, and sharpin the transition across the boundary C. This is formulated as:

F(x1,x2, . . . ,xn)=μΣ_(epq∈E) w _(pq) |x _(p) −x _(q)|+Σ_(p) |u(p)−c₁|² x _(p)+Σ_(p) |u(p)−c ₁|²(1−x _(p))  (1)

where x_(p) is a binary variable that indicates whether a particularpixel p=(x, y) belongs to the cell or to the background. A binaryvariable x_(p) for each pixel p=(x, y)∈Ω is set such that

$\begin{matrix}{x_{p} = \{ \begin{matrix}1 & {{{if}\mspace{14mu} p} \in {U_{i}\Omega_{i}}} \\{0,} & {otherwise}\end{matrix} } & (2)\end{matrix}$

The constants c₁ and c₂ represent the piecewise constant approximationof the input DHM image, w_(pq) represents the weights of the Euclideanlength regularization, and μ is a weighting coefficient that defines thecontribution of the length regularization to the segmentation energy. Arelatively high value for mu may be used to avoid separating the nucleusfrom the rest of the cell.

FIG. 2 provides an example illustration of Segmentation of erythrocytesin optical path difference DHM (OPD-DHM) images. Image 205 shows theinput image, while image 210 shows the delineation (represented by adotted line) of the erythrocyte boundaries is imposed on the image 205.

A normal erythrocyte is generally shaped as a biconcave disk to achievea large surface area-to-volume ratio. For example, FIG. 3 shows ageometry model 300 of a normal erythrocyte. There are four mainparameters that affect the shape of the cell (shown in FIG. 3): thediameter of the cell (D), the minimum dimple thickness (t), the maximumthickness (h) and the diameter of the circle that determines the maximumthickness (d).

Using the geometric understanding presented in FIG. 3, there are severalbi-concave models for the erythrocyte surface representation that may beapplied by Parametric Modeling Component 120 to characterize the shapeand geometry of each erythrocyte. For example, some embodiments utilizethe Evans-Fung Model technique generally known in the art which utilizesthe following equation:

$\begin{matrix}{{z(\rho)} = {\pm {\sqrt{1 - ( \frac{\rho}{R} )^{2}}\lbrack {{c\; 0} + {c\; 1( \frac{\rho}{R} )^{2}} + {c\; 2( \frac{\rho}{R} )^{4}}} \rbrack}}} & (3)\end{matrix}$

where R is the radius of the cell (R=D/2) and p is the horizontaldistance from the center of the cell. To estimate the model parametersc0, c1 and c2 we minimize the sum squared error between the depth map asestimated from the parametric model and the depth observed from the DHM.The sum squared error of the thickness profile is expressed as:

$\begin{matrix}{{SSE}_{thickness} = {\Sigma_{p}( {{z(\rho)} - \frac{u(\rho)}{2}} )}^{2}} & (4)\end{matrix}$

In some embodiments, the parametric modeling of the cell regularizes thecell surface to match the physical models of the erythrocytes andeliminate any distortion caused during the image acquisition process.FIG. 4 provides an example of the parametric modeling of theerythrocytes which illustrates the regularization. In Image 405, thecell surface is depicted as observed from the DHM image. Image 410 showsthe cell surface as characterized by the Evans-Fung model.

After performing the segmentation and the parametric modeling for eachcell, the volume of each RBC may be computed. The RBC volume may then beused as a clinical measurement in and of itself, or it may be used toderive additional clinically significant values. For example, thecalculation of RBC volume may be used in determining mean cell volume(MCV) which, in turn, is a critical parameter in a complete blood count(CBC).

FIG. 5 provides an illustration of a process 500 for calculating cellvolume, according to some embodiments. At step 505, an OPD-DHM image oferythrocytes is received. Next, at step 510, the DHM image is segmentedto extract all the erythrocytes from the background. Segmentation may beperformed, for example, using the combinatorial piece wise constantMumford-Shah energy functional or any similar technique generally knownin art. At step 515, connected component analysis is applied to thesegmentation mask to label the independent erythrocytes.

Continuing with references to FIG. 5, at steps 520-530 a measurementprocess is performed for each erythrocyte. At step 520, the cell surfaceis approximated, for example, using the Evans-Fung technique. The sumsquared differences in Equation 4 are optimized and the model parametersC0, C1 and C2 are computed. Next, at step 525, the model is used toestimate the updated cell thickness z(p) using Equation 3. Then, at step530, the updated cell thickness is used to calculate the cell volume.The output of the process 500 is a volume for each erythrocyte.

The process 500 can be extended to provide the mean hemoglobin contentof the cell as well. For this, a conventional hematology analyzer may beused for calibration of mean cell volume and conversion of opticaldensity map based volume to the average hemoglobin content.

Differential blood tests measure the percentage of each type of whiteblood cell type in a blood cell sample. Identifying each type of whiteblood cell is a crucial preliminary step to blood differential analysisthat is used to diagnose diseases such as infections, anemia andLeukemia. To address this step, the system described herein may beapplied to differentiate among the different white blood cell types.Specifically, the described system aims to differentiate five differenttypes of white blood cells, namely: monocytes, basophils, neutrophils,eosinophils and lymphocytes. Briefly, a pipeline of processing the whiteblood cells comprises the following three steps. The first step ispre-processing where various types of white blood cells are identifiedand isolated. The second step is training where a classifier is trainedfrom the extracted cells. The third step is classification where aclassifier is used to categorize unseen cells. Each of these steps willnow be discussed in detail.

FIG. 6 provides an illustration of a pre-processing framework 600 forclassifying white blood cells, as it may be applied in some embodiments.During the pre-processing step, a pipeline is used to prepare thetraining patches for the classifier training step. Initially, athreshold is applied to an input image 605 to capture the bright spots(that are highly likely to be cells). After thresholding, a connectedcomponent analysis is applied to the thresholded image 610. Then, asillustrated in image 615, the component size is calculated for eachconnected component. A component is rejected if its size is below orabove predefined thresholds t1 and t2. The rejected components areexcluded from training and testing. The patches (patch is a rectangularbox including the connected component), including the remainingcomponents are used to train the classifier (e.g., a K-Nearest Neighborclassifier).

Following the pre-processing framework 600, various machine learningalgorithms may be used for performing five part differential on DHMimages of white blood cells. For example, in some embodiments, atexton-based approach is used, where the textural characteristics of DHMimage of cells are used as the main discriminating feature. In otherembodiments, a more data driven approach is employed, where a dictionaryof image patches is learned and used to represent the entire image as ahistogram. The histogram is then used as the main feature forclassification between various cell sub-types. In other embodiments,classification is based on a convolutional neural network with multiplelayers to perform feature extraction, selection, and classification atonce with a multi-layer network. Each of these different approaches isdescribed in detail below.

As is generally understood in the art, image classification algorithmsare trained using a data set of representative images. One example of atraining data set is illustrated in FIG. 7. In this example, cell imagesare labelled in one of four categories: monocyte, basophil, neutrophil,and lymphocytes. It should be noted that this training set is but oneexample of a data that may be used in training the classifier. Forexample, other training sets employed in some embodiments may includeadditional categories (e.g., eosinophils). Additionally (oralternately), the categories could have a finer level of granularity.

In embodiments where the texton-based approach is used forclassification, the extracted patches that include the preprocessed cellimages are used to train a K-Nearest Neighbor (K-NN) classifier. Theclassifier utilized texton based texture features extracted from eachpatch. Textons are the representation of small texture features by acollection of filter bank responses. Texton based texture classifiersuse the histograms of the textons to characterize the texture by thefrequency of the textons in a particular patched as compared to thetextons of the training data set. The texton features are used, inparticular, because it is pathologically proven that the granularity ofcell nuclei differ in the different cell types and this can be capturedusing texture features.

To illustrate the texton-based approach, consider a combination of thetexton based texture feature representation with a simple K-NNclassifier. FIG. 8A provides a table showing an example of a data setthat may be used for training and testing for each cell type. Theclassification rate obtained for this example dataset in all thepairwise classifications varies from 75% to 95%. FIG. 8B shows pairwiseclassification results obtained using texton features and KNN classifierfor this sample case.

To provide a multi-label classifier using the previous pairwiseclassification, one or more pairwise classifiers may be combined usingvoting. Ideally four classifiers should agree on the class label and 6classifiers would provide random labeling. For example, if 10classifiers are denoted as C1 to C10, the final class label L for anunknown sample S can be represented as the mode (most frequent value) ofthe labels of the 10 classifiers. In other words, the majority vote ofthe 10 pairwise classifiers is labeled for the multi-labelclassification problem. Combining all the pairwise classification to onemulti-label classifier yielded a 76.5% correct classification rate forthe example dataset set presented in FIG. 8A.

In some embodiments, a bag of visual words (BOW) approach may be used tosolve this multi-class based image classification problem. In the BOWapproach, the global image features are represented by vectors ofoccurrence counts of visual words (a histogram over a vocabularydictionary based on local image feature). These global image featuresare then used for classification. The pipeline may be divided into threestages: offline vocabulary learning, image classification training andtesting. In the offline vocabulary learning stage, a visual vocabularydictionary may be trained using hierarchical k-means and SIFT(scale-invariant feature transform) descriptor as local image feature.For classification, one against one n-label supporting vector machine(SVM) may be utilized. To avoid an over-fitting problem, two approachesmay be employed. First, each training image may be perturbed at randomdegree. Secondly, SVM parameters may be determined for the kernel usingcross validation on the training data set.

SIFT descriptor describes invariant local image structure and capturinglocal texture information. Dense SIFT descriptors may be computed forevery n_(s) pixels of each image (w×h, where w and h are image width andheight respectively. For example, a 128 dimension SIFT descriptor may beused and, thus, there are about (w/n_(s))×(h/n_(s))×128 local imagefeatures per image. FIG. 9 provides an example of local image featuresampling, as it may be applied in some embodiments. In this example,each white dot represents a location where 128 dimension SIFT descriptoris computed.

In some embodiments, a modified vocabulary tree structure is utilized toconstruct a visual vocabulary dictionary. The vocabulary tree defines ahierarchical quantization using a hierarchical k-means clustering. FIG.10 shows a complete binary (k=2) search tree structure which representsa vocabulary dictionary structures. The node 1005 is a visual clustercenter. 2^(n) ^(d) leaf nodes are finally used as visual vocabularywords. n_(d) is the depth of the binary tree. In the vocabulary treelearning stage, first the initial k-means algorithm is applied on to thetraining data (a collection of SIFT descriptors derived from trainingdata set) and then partitioned into 2 groups, where each group comprisesSIFT descriptors closest to the cluster center. This process is thenrecursively applied until tree depth reaches n_(d). In the online stage,a SIFT descriptor (a vector) is passed down the tree by each level viacomparing this feature vector to the 2 cluster centers and choosing theclosest one. The visual word histogram is computed for all the denseSIFT descriptors on each image.

FIG. 11 provides a visualization of the workflow for the transformationfrom local image features (dense SIFT descriptor) to global imagefeatures (vocabulary histogram). The results obtained by combining SIFTfeatures with SVM classification for an example dataset are depicted inFIG. 12. Combining these pairwise classifiers yielded an 84% correctclassification rate for the 5-type classification problem.

In some embodiments, a Deep Learning (DL) architecture is used toperform the 5-type classification. While SVM is mainly formulated forbinary classification, DL is inherently a multi-label classifier. Toillustrate the DL classification technique, the convolutional networkwas directly trained for the multi-label classification problem. 500cells were used for training, 100 for each category and the rest of thecells were used for testing.

For this example DL application, an auto-encoder convolutional neuralnetwork (CNN) was used to train the marginal space learning (MSL)classifier. FIG. 13 shows the structure of a feed-forward neural networkwith one hidden layer (also referred to as an “AE”). Ignoring the biasterm (the nodes labeled as “+1” in FIG. 13), the input and output layershave the same number of nodes. The objective of such auto encoders is tolearn a transfer function between the input layer (features thatrepresent the images) and the output layer (the labels for the image).If the hidden layer has a size equal or larger than the input layer,potentially, an AE may learn an identity transformation. To prevent sucha trivial solution, previously, an AE is set up with a hidden layer withfewer nodes than the input. Recently, denoising auto-encoder (DAE) wasproposed to learn a more meaningful representation of the input. Acertain percentage (e.g., 50%) of input nodes are randomly picked to bedisturbed (e.g., set the value to zero) and the DAE is required toreconstruct the original input vector given a contaminated observation.With DAE, the hidden layer may have more nodes than the input to achievean over-complete representation. After training an AE, the output layeris discarded and another AE is stacked using the activation response ofthe already trained hidden layer as input to the new AE. This processcan be repeated to train and expand a network layer by layer. Afterpre-training, the output of the hidden layers can be treated ashigh-level image features to train a classifier. Alternatively, we canadd one more layer for the target output and the whole network can berefined using back-propagation.

After the classification is done, the various categories of white bloodcells may be presented to the pathologist in a graphical user interface(GUI) for final confirmation. The probability of a given cell belongingto a certain sub-type may be calculated and presented numerically orgraphically to the pathologist during the final check.

FIG. 14 shows a GUI which provides for results visualization andfacilitates user interaction and correction, according to someembodiments. As shown in the FIG. 14, the pathologist will have a chanceto make a modification based on the DHM image of the cells. The resultsof the automatic blood differential is depicted at the top two rows 1405displaying the percentage of each blood cell type and the possiblediagnosis based on these percentages. The bottom part of the FIG. 1410shows how the user can modify the results. The system shows the top cellcandidates for user interactions. These cells are chosen and sortedbased on the difference of the top two probabilities. The systemdisplays the extracted cell (extracted by our preprocessing pipeline)and displays the probability of it belonging to each WBC type. The usercan accept the label or choose a new label by simply marking a checkbox. If the user changes the label, the system may automatically updatethe counts, the percentages and the possible diagnosis, which changesthe top row and displays the new results.

Additionally, in order for pathologist to best be able to review theimages, in some embodiments, a pseudo-colorization scheme is utilizedwhich converts the DHM images into an image with a color pattern similarto cell images stained for conventional staining methodologies such asthe Wright and Giemsa methodologies. For example, Giemsa-Wright stainsuse solutions which include eosin Y, azure B, and methylene blue forstaining. These solutions bind to the constituents of the cellsincluding nucleus and granules differently and thereby provide a morepronounced coloring which is essential for visual inspection of thecells. From a DHM image of a cell, we obtain different optical densitypatterns for the cell constituents and that could be used as a featureto perform colorization. The method that we envision is based on havingmatched pairs of cells which are imaged both with DHM and Giemsastaining respectively. In some embodiments a simple regression functionis used, which can be implemented using a machine learning techniquesuch as a convolutional neural network to map optical density maps fromDHM to the RGB color scheme consistent with Giemsa-Wright stainingprotocol. The regression map could work either on the single pixel orgroups of pixels (i.e., image patches). In addition, a Markov randomfield based regularization may be utilized to make sure that theneighboring pixels with similar optical density will be coloredsimilarly. Aside from Giemsa and Wright stains, other staining protocolscan be learned based on set of matching pairs of cellular images and thestained version can be digitally reproduced from the DHM images.

In some embodiments, the mean cell volume computation for RBC describedherein and/or the WBC five part differential may be used as keymeasurements for a Complete Blood Count (CBC) test. As is understood inthe art, a CBC test evaluates an individual's overall health and may beused in the detection of blood-related disorders such as anemia,infection and leukemia. So, for example, abnormal increases or decreasesin particular WBC counts may be used as an indicator of an underlyingmedical condition that calls for further evaluation.

Various other extensions, enhancements, or other modifications may bemade or added to the techniques described herein to provide additionalfunctionality. For example, in some embodiments, the five partdifferential methodology for white blood cell sub-typing is applied onraw fringe pattern images prior to the reconstruction as well. In orderto increase the overall accuracy of either RBC volume or WBCclassification, multiple images of the same cell could be used and theresults could be averaged. The systems and methods described herein mayalso be applied to clinical applications outside the cell classificationspace. For example, the size calculation and classification of variousobjects based on the methodology described can be extended for urineanalysis applications, where the size of urine sediments are measuredand various kinds are identified by analyzing the DHM images using themethodology described. Additionally, given the latest advancements inDHM technology—particularly reductions in size, complexity andcost—these (and other) applications could be performed within a clinicalenvironment or at the point of care (in a decentralized manner).

FIG. 15 illustrates an exemplary computing environment 1500 within whichembodiments of the invention may be implemented. For example, thiscomputing environment 1500 may be configured to execute one or more ofthe components of the framework 100 for processing DHM imagesillustrated in FIG. 1. Additionally (or alternatively), this computingenvironment 1500 may be configured to perform one or more of theprocesses described herein (e.g., the process 500 for calculating cellvolume shown in FIG. 1. The computing environment 1500 may includecomputer system 1510, which is one example of a computing system uponwhich embodiments of the invention may be implemented. Computers andcomputing environments, such as computer system 1510 and computingenvironment 1500, are known to those of skill in the art and thus aredescribed briefly here.

As shown in FIG. 15, the computer system 1510 may include acommunication mechanism such as a bus 1521 or other communicationmechanism for communicating information within the computer system 1510.The computer system 1510 further includes one or more processors 1520coupled with the bus 1521 for processing the information. The processors1520 may include one or more central processing units (CPUs), graphicalprocessing units (GPUs), or any other processor known in the art.

The computer system 1510 also includes a system memory 1530 coupled tothe bus 1521 for storing information and instructions to be executed byprocessors 1520. The system memory 1530 may include computer readablestorage media in the form of volatile and/or nonvolatile memory, such asread only memory (ROM) 1531 and/or random access memory (RAM) 1532. Thesystem memory RAM 1532 may include other dynamic storage device(s)(e.g., dynamic RAM, static RAM, and synchronous DRAM). The system memoryROM 1531 may include other static storage device(s) (e.g., programmableROM, erasable PROM, and electrically erasable PROM). In addition, thesystem memory 1530 may be used for storing temporary variables or otherintermediate information during the execution of instructions by theprocessors 1520. A basic input/output system (BIOS) 1533 containing thebasic routines that help to transfer information between elements withincomputer system 1510, such as during start-up, may be stored in ROM1531. RAM 1532 may contain data and/or program modules that areimmediately accessible to and/or presently being operated on by theprocessors 1520. System memory 1530 may additionally include, forexample, operating system 1534, application programs 1535, other programmodules 1536 and program data 1537.

The computer system 1510 also includes a disk controller 1540 coupled tothe bus 1521 to control one or more storage devices for storinginformation and instructions, such as a hard disk 1541 and a removablemedia drive 1542 (e.g., floppy disk drive, compact disc drive, tapedrive, and/or solid state drive). The storage devices may be added tothe computer system 1510 using an appropriate device interface (e.g., asmall computer system interface (SCSI), integrated device electronics(IDE), Universal Serial Bus (USB), or FireWire).

The computer system 1510 may also include a display controller 1565coupled to the bus 1521 to control a display 1566, such as a cathode raytube (CRT) or liquid crystal display (LCD), for displaying informationto a computer user. The computer system includes an input interface 1560and one or more input devices, such as a keyboard 1562 and a pointingdevice 1561, for interacting with a computer user and providinginformation to the processor 1520. The pointing device 1561, forexample, may be a mouse, a trackball, or a pointing stick forcommunicating direction information and command selections to theprocessor 1520 and for controlling cursor movement on the display 1566.The display 1566 may provide a touch screen interface which allows inputto supplement or replace the communication of direction information andcommand selections by the pointing device 1561.

The computer system 1510 may perform a portion or all of the processingsteps of embodiments of the invention in response to the processors 1520executing one or more sequences of one or more instructions contained ina memory, such as the system memory 1530. Such instructions may be readinto the system memory 1530 from another computer readable medium, suchas a hard disk 1541 or a removable media drive 1542. The hard disk 1541may contain one or more datastores and data files used by embodiments ofthe present invention. Datastore contents and data files may beencrypted to improve security. The processors 1520 may also be employedin a multi-processing arrangement to execute the one or more sequencesof instructions contained in system memory 1530. In alternativeembodiments, hard-wired circuitry may be used in place of or incombination with software instructions. Thus, embodiments are notlimited to any specific combination of hardware circuitry and software.

As stated above, the computer system 1510 may include at least onecomputer readable medium or memory for holding instructions programmedaccording to embodiments of the invention and for containing datastructures, tables, records, or other data described herein. The term“computer readable medium” as used herein refers to any medium thatparticipates in providing instructions to the processor 1520 forexecution. A computer readable medium may take many forms including, butnot limited to, non-volatile media, volatile media, and transmissionmedia. Non-limiting examples of non-volatile media include opticaldisks, solid state drives, magnetic disks, and magneto-optical disks,such as hard disk 1541 or removable media drive 1542. Non-limitingexamples of volatile media include dynamic memory, such as system memory1530. Non-limiting examples of transmission media include coaxialcables, copper wire, and fiber optics, including the wires that make upthe bus 1521. Transmission media may also take the form of acoustic orlight waves, such as those generated during radio wave and infrared datacommunications.

The computing environment 1500 may further include the computer system1510 operating in a networked environment using logical connections toone or more remote computers, such as remote computer 1580. Remotecomputer 1580 may be a personal computer (laptop or desktop), a mobiledevice, a server, a router, a network PC, a peer device or other commonnetwork node, and typically includes many or all of the elementsdescribed above relative to computer system 1510. When used in anetworking environment, computer system 1510 may include modem 1572 forestablishing communications over a network 1571, such as the Internet.Modem 1572 may be connected to bus 1521 via user network interface 1570,or via another appropriate mechanism.

Network 1571 may be any network or system generally known in the art,including the Internet, an intranet, a local area network (LAN), a widearea network (WAN), a metropolitan area network (MAN), a directconnection or series of connections, a cellular telephone network, orany other network or medium capable of facilitating communicationbetween computer system 1510 and other computers (e.g., remote computer1580). The network 1571 may be wired, wireless or a combination thereof.Wired connections may be implemented using Ethernet, Universal SerialBus (USB), RJ-11 or any other wired connection generally known in theart. Wireless connections may be implemented using Wi-Fi, WiMAX, andBluetooth, infrared, cellular networks, satellite or any other wirelessconnection methodology generally known in the art. Additionally, severalnetworks may work alone or in communication with each other tofacilitate communication in the network 1571.

As one application of the exemplary computing environment 1500 to thetechnology described herein, consider an example system for analyzingDHM data for hematology applications which includes a network component,a modeling processor, and a GUI. The networking component may includenetwork interface 1570 or some combination of hardware and softwareoffering similar functionality. The networking component is configuredto communicate with a DHM system to retrieve DHM images. Thus, in someembodiments, the networking component may include a specializedinterface for communicating with DHM systems. The modeling processor isincluded in a computing system (e.g. computer system 1510) and isconfigured with instructions that enable it to train a classifier forcell types present in cell images extracted from DHM images received viathe networking component. The modeling processor may include additionalfunctionality, as described in this disclosure, to support this task(e.g., segmentation, identifying connected components, etc.). Themodeling processor is further configured to use the classifier todetermine the probability that new cell images belong to one of thetypes used to train the classifier. The GUI may then be presented on adisplay (e.g., display 1566) for review by a user.

The embodiments of the present disclosure may be implemented with anycombination of hardware and software. In addition, the embodiments ofthe present disclosure may be included in an article of manufacture(e.g., one or more computer program products) having, for example,computer-readable, non-transitory media. The media has embodied therein,for instance, computer readable program code for providing andfacilitating the mechanisms of the embodiments of the presentdisclosure. The article of manufacture can be included as part of acomputer system or sold separately.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

An executable application, as used herein, comprises code or machinereadable instructions for conditioning the processor to implementpredetermined functions, such as those of an operating system, a contextdata acquisition system or other information processing system, forexample, in response to user command or input. An executable procedureis a segment of code or machine readable instruction, sub-routine, orother distinct section of code or portion of an executable applicationfor performing one or more particular processes. These processes mayinclude receiving input data and/or parameters, performing operations onreceived input data and/or performing functions in response to receivedinput parameters, and providing resulting output data and/or parameters.

A graphical user interface (GUI), as used herein, comprises one or moredisplay images, generated by a display processor and enabling userinteraction with a processor or other device and associated dataacquisition and processing functions. The GUI also includes anexecutable procedure or executable application. The executable procedureor executable application conditions the display processor to generatesignals representing the GUI display images. These signals are suppliedto a display device which displays the image for viewing by the user.The processor, under control of an executable procedure or executableapplication, manipulates the GUI display images in response to signalsreceived from the input devices. In this way, the user may interact withthe display image using the input devices, enabling user interactionwith the processor or other device.

The functions and process steps herein may be performed automatically orwholly or partially in response to user command. An activity (includinga step) performed automatically is performed in response to one or moreexecutable instructions or device operation without user directinitiation of the activity.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of the invention to accomplish the same objectives. Althoughthis invention has been described with reference to particularembodiments, it is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the invention. Asdescribed herein, the various systems, subsystems, agents, managers andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112, sixth paragraph,unless the element is expressly recited using the phrase “means for.”

1. A method for analyzing digital holographic microscopy (DHM) data forhematology applications to perform white blood cell differentiation, themethod comprising: receiving a plurality of DHM images acquired using adigital holographic microscopy system; identifying one or more connectedcomponents in each of the plurality of DHM images; generating one ormore training white blood cell images from the one or more connectedcomponents; training a classifier to identify a plurality of white bloodcell types using the one or more training white blood cell images as aninput to the classifier; extracting a new white blood cell image from anew DHM image; applying the classifier to the new white blood cell imageto determine a plurality of probability values, each respectiveprobability value corresponding to one of the plurality of white bloodcell types; and presenting the new white blood cell image and theplurality of probability values in a graphical user interface.
 2. Themethod of claim 1, further comprising: prior to identifying the one ormore connected components, applying a thresholding to the each of theplurality of DHM images to highlight bright spots in each respective DHMimage.
 3. The method of claim 2, wherein the one or more connectedcomponents comprise a set of connected components and the method furthercomprises: removing one or more small connected components from the oneor more connected components after identifying the one or more connectedcomponents, wherein each small connected component has a size below apredetermined threshold value.
 4. The method of claim 1, wherein theclassifier is a K-Nearest Neighbor (K-NN) classifier.
 5. The method ofclaim 4, wherein the classifier uses texton-based texture featuresextracted from each of the plurality of DHM images to classify the newDHM image.
 6. The method of claim 1, wherein the classifier is a deeplearning classifier trained using an auto-encoder convolutional neuralnetwork (CNN).
 7. The method of claim 6, the CNN includes an inputlayer, a hidden layer and an output layer, wherein the input layer andthe output layer have the same number of nodes, and the hidden layer hasfewer nodes than the input layer.
 8. The method of claim 1, wherein theplurality of white blood cell types include a monocyte, a basophil, aneutrophil, an eosinophil and a lymphocyte.
 9. The method of claim 1,further comprising: converting the new white blood cell image into animage with a color pattern through a pseudo-colorization scheme.
 10. Themethod of claim 1, further comprising: determining a mapping betweenoptical density and coloring associated with a staining protocol; anddetermining optical density information associated with the new whiteblood cell image; and prior to presenting the new white blood cellimage, colorizing the new white blood cell image using the mapping andthe optical density information.
 11. The method of claim 10, wherein thestep of colorizing the new white blood cell image further comprising:colorizing neighboring pixels of the new white blood cell image withsimilar optical density similarly through Markov random field basedregularization.
 12. The method of claim 1, further comprising:performing a complete blood cell (CBC) test using the plurality ofprobability values.
 13. An article of manufacture for analyzing digitalholographic microscopy (DHM) data for hematology applications to performwhite blood cell differentiation, the article of manufacture comprisinga non-transitory, tangible computer-readable medium holdingcomputer-executable instructions for performing a method comprising:receiving a plurality of DHM images acquired using a digital holographicmicroscopy system; identifying one or more connected components in eachof the plurality of DHM images; generating one or more training whiteblood cell images from the one or more connected components; training aclassifier to identify a plurality of white blood cell types using theone or more training white blood cell images as an input to theclassifier; extracting a new white blood cell image from a new DHMimage; applying the classifier to the new white blood cell image todetermine a plurality of probability values, each respective probabilityvalue corresponding to one of the plurality of white blood cell types;and presenting the new white blood cell image and the plurality ofprobability values in a graphical user interface.
 14. The article ofmanufacture of claim 13, wherein the method further comprises: prior toidentifying the one or more connected components, applying athresholding to the plurality of DHM images to highlight bright spots ineach respective DHM image.
 15. The article of manufacture of claim 14,wherein the one or more connected components comprise a set of connectedcomponents and the method further comprises: removing one or more smallconnected components from the one or more connected components afteridentifying the one or more connected components, wherein each smallconnected component has a size below a predetermined threshold value.16. The article of manufacture of claim 13, wherein the classifier usestexton-based texture features extracted from each of the plurality ofDHM images to classify the new DHM image.
 17. The article of manufactureof claim 13, wherein the classifier is a visual vocabulary dictionarytrained using hierarchical k-means and a scale-invariant featuretransform (SIFT) descriptor as a local image feature.
 18. The article ofmanufacture of claim 13, wherein the classifier is a marginal spacelearning (MSL) classifier trained using an auto-encoder convolutionalneural network (CNN).
 19. The article of manufacture of claim 13,wherein the method further comprises: determining a mapping betweenoptical density and coloring associated with a staining protocol; anddetermining optical density information associated with the new whiteblood cell image; and prior to presenting the new white blood cellimage, colorizing the new white blood cell image using the mapping andthe optical density information.
 20. A system for analyzing digitalholographic microscopy (DHM) data for hematology applications to performwhite blood cell differentiation, the system comprising: a networkingcomponent configured to communicate with a digital holographicmicroscopy system to retrieve a plurality of training DHM images and atest DHM image; a modeling processor configured to: identify one or moreconnected components in each of the plurality of training DHM images,generate one or more training white blood cell images from the one ormore connected components, train a classifier to identify a plurality ofwhite blood cell types using the one or more training white blood cellimages as an input to the classifier, extract a test white blood cellimage from the test DHM image, and apply the classifier to the testwhite blood cell image to determine a plurality of probability values,each respective probability value corresponding to one of the pluralityof white blood cell types; and a graphical user interface configured topresent the test white blood cell image and the plurality of probabilityvalues.